Deposit interest rates, asset risk and bank failure in Croatia ppt

Deposit interest rates, asset risk and bank failure in Croatia


Evan Kraft,
Advisor to the Governor
Croatian National Bank
Trg hrvatskih velikana 3
10000 Zagreb
Croatia
tel: (3851) 4564-858
fax: (3851) 4564-784
email:


Tomislav Galac, Director,
Financial Stability Department
Croatian National Bank
Trg hrvatskih velikana 3
10000 Zagreb
Croatia
tel: (3851) 4564-842
fax: (3851) 4564-784
email:



1
Abstract:

During the 1980’s and 1990’s, financial liberalization became an almost universally-
accepted policy prescription. Large numbers of countries eased licensing, deregulated interest

rates and dismantled systems of directed lending. However, banking system crises, first in the
southern cone of Latin America in the early 1980’s and later in the U.S., Scandinavian
countries and a large set of emerging market economies, raised questions about the links
between financial liberalization and instability. In particular, Hellman, Murdoch and Stiglitz
(2000) question the wisdom of complete deregulation of deposit interest rates, arguing that
this can facilitate “purchasing market share” to fund “gambling.”
The transition countries of Central and Eastern Europe provide an interesting
laboratory to test these arguments. Starting in the early 1990’s, these countries rapidly
liberalized their banking markets, removing restrictions on entry, asset composition and
interest rates. For this reason, the experience of such countries may help confirm whether the
U.S. experience of the 1980’s was typical.
In this paper, we examine the experience of Croatia, which liberalized its banking
regulations in the early 1990’s. After the end of the wars surrounding the break-up of former
Yugoslavia, Croatia experienced rapid growth in the number of banks, strong deposit growth
and substantial increases in deposit interest rates in the period 1995-98. This buoyant period
was punctuated by the failures of numerous medium-sized banks in 1998 and 1999.
Our argument is that high deposit interest rates helped fund the expansion of risk-
loving banks, and in fact were a fairly reliable signal of increased bank asset risk. We proceed
in two steps. First, using panel regression techniques, we show that banks were able to
increase deposit growth, and thus fund rapid expansion, by raising interest rates in the pre-
crisis period. We also show that the interest-elasticity of deposits completely vanished during
the banking crisis.
Second, we provide a set of predictive models of bank failures. These models show
that deposit interest rates were one of the most significant variables predicting bank failures.
High risk banks—the ones that eventually failed—often offered higher deposit interest rates
than low risk banks.
Having shown that high deposit interest rates were a source of funding for risky banks,
and that high deposit interest rates are correlated with eventual failure, we end the paper with
a discussion of policy implications.

Keywords: interest rate regulation, banking crisis, bank failure models, financial
liberalization.

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1. Introduction

During the 1980’s and 1990’s, financial liberalization became an almost universally-
accepted policy prescription. Large numbers of countries eased licensing, deregulated interest
rates and dismantled systems of directed lending. However, banking system crises, first in the
southern cone of Latin America in the early 1980’s (Diaz-Alejandro 1985), and later in the
U.S., (White 1991, Kane 1989) Scandinavian countries (Nyberg and Vihriala 1994, Vihriala
1996) and a large set of emerging market economies, raised questions about the links between
financial liberalization and instability (for cross-country econometric evidence see Demirguc-
Kunt and Detriagache 1998, 1999). While there are strong arguments and some evidence to
argue that financial liberalization is beneficial in the long-term (Allen and Gale 2003,
Ranciere, Tornell and Westermann 2003) there is much controversy about the medium-term
costs and the optimal approach to regulation under liberalized conditions.
A crucial component of financial liberalization is the liberalization of interest rate
setting. With the lifting of Regulation Q in 1980 in the United States, intellectual fashion
moved against the regulation of deposit interest rates. However, in the decade that followed
the lifting of regulation Q, the U.S. experience provided considerable anecdotal evidence
about the negative effects of unlimited freedom to set deposit interest rates. Some aggressive
banks used high deposit interest rates to fund their risky lending strategies. And the high
deposit interest rates of these banks created a negative externality by forcing less risk-loving
banks to raise their deposit rates to retain deposits, thus squeezing bank profits and creating a
secondary impulse for less risky banks to actually increase the riskiness of their portfolio.
Despite this, deregulation of deposit interest rates became a standard element of the financial
liberalization package adopted by large numbers of countries.
Keeley (1990) argues that the increase in risk-taking following deregulation was the
result of the combination of unrestricted competition with fixed-premium deposit insurance.

Increased competition erodes franchise value. Under fixed-premium deposit insurance, this
increases the attractiveness of added risk, since greater probability of failure is not reflected in
higher premia and thus does not increase the extent of losses suffered by the owner under
failure. At the same time, added risk implies higher earnings under favorable outcomes, and
thus increases the bank’s capital conditional upon survival. Keeley demonstrates that banks
with greater market power maintain higher market-value capital-asset ratios and enjoyed
lower interest rates on large, uninsured certificates of deposit. Reversing this, the erosion of
franchise value caused by deregulation would lead to higher deposit interest rates.
Hellman, Murdoch and Stiglitz (2000) provide a theoretical argument to show that, in
an environment with only capital adequacy regulation and no regulation of interest rates,
banks may have an incentive to bid up deposit interest rates so as to gain the funding to
“gamble” (increase asset risk). Only a combination of capital adequacy regulation and deposit

3
interest rate limitations can implement the Pareto-optimal allocation under all circumstances.
Capital adequacy regulation alone tends to fail when competition is strong, i.e precisely in
deregulated banking systems. Hellman et al consider systems with and without deposit
insurance, but they only consider fixed-premium insurance, and acknowledge that
“sophisticated fee schemes can be used to reduce moral hazard”.
This leaves open the question of whether the levying of risk-adjusted deposit
insurance premia could eliminate incentives to excessive risk-taking. Chan, Greenbaum and
Thakor (1992) argue that both incentive and information problems make fairly-priced deposit
insurance unfeasible. This question has been hotly debated since then, but the thrust of the
literature seems to lean against the feasibility of completely eliminating risk-taking via risk-
adjusted deposit insurance premia (see, for example, Flannery 1991, John and John 1991,
Crane 1995, Kupiec and O’Brien 1997, and Freixas and Rochet 1998. Galac 2004 provides an
overview). Based on this, we hold that risk-adjusted premia, although possibly desirable,
cannot be a panacea that wholly eliminates the problem of “market-stealing” increases of
deposit interest rates to fund “gambling.”
Taken together, all this points to a connection between “excessive” competition in the

deposit market and suboptimal increases in risk taking. The transition countries of Central and
Eastern Europe provide an interesting laboratory to test these arguments. Starting in the early
1990’s, these countries rapidly liberalized their banking markets, removing restrictions on
entry, asset composition and interest rates. For this reason, the experience of such countries
may help confirm whether the U.S. experience of the 1980’s was typical.
In this paper, we examine the experience of Croatia, which enacted rather liberal
regulations regarding entry, asset composition and interest rates in the early 1990’s. After the
end of the wars surrounding the break-up of former Yugoslavia, Croatia experienced rapid
growth in the number of banks, strong deposit growth and substantial increases in deposit
interest rates in the period 1995-98. This buoyant period was punctuated by the failures of
numerous medium-sized banks in 1998 and 1999.
Our argument is that high deposit interest rates helped fund the expansion of risk-
loving banks, and in fact were a fairly reliable signal of increased bank asset risk. We proceed
in two steps. First, using panel regression techniques, we provide evidence to show that banks
were able to increase deposit growth, and thus fund rapid expansion, by raising interest rates
in the pre-crisis period. We show that the interest-elasticity of deposits was positive and
significant, so that “market-stealing” behavior a la Hellman et al was feasible. We also show
that the interest-elasticity of deposits completely vanished during the banking crisis as a flight
to quality occurred.
Second, we provide a set of predictive models of bank failures. These models show
that high deposit interest rates were one of the most significant variables predicting bank

4
failures. That is, high risk banks—the ones that eventually failed—often offered higher
deposit interest rates than low risk banks.
Having shown that high deposit interest rates were a source of funding for risky banks,
and that high deposit interest rates are correlated with eventual failure, we end the paper with
a discussion of policy implications. While we note that the first-best policy would be to use
high deposit interest rates as a signal of increased risk, and to initiate appropriate corrective
action at such banks, we argue that, when supervision capabilities are weak and/or legislation

prevents adequate, timely corrective action, some form of market-conforming regulations to
prevent “market-stealing” via increased deposit interest rates may be an appropriate
safeguard.
The paper proceeds as follows. Section 2 provides a brief overview of the
liberalization of the banking market in Croatia in the 1990’s and the dynamics of growth and
crisis. Section 3 offers an econometric analysis of deposit growth. Section 4 presents models
of failure and elucidates the role of deposit interest rates in failures. Section 5 provides a
discussion of policy options and conclusions.

2. Liberalization, growth and crisis in the Croatian banking sector

The liberalization of the banking system in Croatia started while Croatia was still part
of the former socialist Yugoslavia in 1989-90. A new banking law was enacted, allowing
relatively free entry, and interest rates were deregulated. Bank supervision was established,
but its effectiveness in the early years was limited.
Liberalization took place under conditions of war, accompanied by high inflation and
sharp declines in output. A macroeconomic stabilization program implemented in October
1993 succeeded in bringing inflation under control, and real GDP growth began in 1994.
Decisive military actions in May and August 1995, and the signing of the Dayton Peace
Agreement in neighboring Bosnia and Herzegovina in November 1995 and the Erdut
Agreement in late 1996 ended the period of conflict and brought about a sharp decline in
political risk.
The number of banks grew rapidly, even during the war, rising from 22 in 1991 to
some 61 in 1997. In addition, by 1997, 36 savings banks, with limited licenses, were also
operating. Deposits began growing strongly in 1995. Growth came partly as a result of the
return of deposits placed in foreign banks by Croatian citizens during the war. In addition,
growing confidence in the banking system began to attract deposits held “in mattresses”.

Table 1: Banking and Macroeconomic Overview
1990 1995 1996 1997 1998 1999 2000 2001 2002 2003

Number of banks 22 54 58 60 60 53 46 44 46 42
Foreign banks 0 1 5 7 10 13 20 24 23 19
Foreign bank assets share 0 1.0 1.0 4.0 6.7 39.9 84.1 89.3 90.2 91.0

5
Real GDP growth, % 6.8 6.0 6.8 2.5 -0.9 2.9 4.4 5.2 4.3
Inflation, % 3.8 3.4 3.8 5.4 4.4 7.4 2.3 1.9 1.7

1996 in particular witnessed a substantial increase in deposit interest rates at some
banks. Interest rates on domestic currency deposits rose dramatically in late 1995 and early
1996. (see Figure 1) However, it should be noted that these deposits accounted for a very
small portion of the total. Interest rates on fx deposits, the bulk of deposits, rose substantially
later in the year. A number of banks offered interest rates on deposits in Deutschmarks that
exceed comparable rates in Germany by some 800 to 1000 basis points. (see Kraft 1999 for
details)

Figure 1: Average bank deposit interest rates
0,00
2,00
4,00
6,00
8,00
10,00
12,00
14,00
16,00
06.94.
11.94.
04.95.
09.95.

02.96.
07.96.
12.96.
05.97.
10.97.
03.98.
08.98.
01.99.
06.99.
11.99.
04.00.
09.00.
02.01.
07.01.
12.01.
05.02.
10.02.
03.03.
08.03.
01.04.
kuna time deposits
fx time deposits

Deposits grew explosively in this period, with annual growth rates exceeding 50% through
most of 1996 and all of 1997 (see Figure 2). Both kuna and fx deposits grew rapidly.

Figure 2: Rate of growth of non-transactions deposits, % yoy
-10
0
10

20
30
40
50
60
70
80
06.
9
5
.
11.
9
5.
04.
9
6.
09.96.
02.97.
07.97.
12.97.
0
5
.98.
1
0
.98.
0
3
.99.

0
8.
99.
0
1.
00.
06.
0
0
.
11.
0
0
.
04.
0
1.
09.
0
1.
02.
0
2.
07.02.
12.02.
05.03.
1
0
.03.
0

3
.04.



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At the same time, lending surged, reaching a peak growth rate of 44% in 1997. Such
rapid growth suggested the presence of increased risk taking, and indeed, in 1998, several
bank failures occurred. The failures continued into 1999, with a total of 16 banks accounting
for approximately 20% of 1997 total banking assets failing in 1998-99. Deposit growth came
to a halt, and aggregate deposits actually fell during the height of the crisis in February-May
1999. During the crisis, there were signs of a reallocation of deposits towards the foreign
banks, as some domestic banks experienced substantial withdrawals.
The crisis was overcome through a combination of bankruptcies, lender-of-last resort
actions by the central bank, and a turnaround in the macroeconomic situation starting in the
second half of 1999. The sale of four banks that had been seized by the government to foreign
strategic partners in late 1999 and early 2000 helped further consolidate the situation.

3. Econometric analysis of deposit growth

The brief background sketched out in section 2 suggests that risk-loving banks used
increases in deposit interest rates in the expansionary period of 1995-97 to fund rapid lending
growth. However, once bank failures began, a flight to quality occurred, in which interest
rates were no longer the decisive factor in deposit allocation.
To test whether this picture is accurate, in this section we build a panel model of
depositor behavior and test it on the Croatian data. Our dependent variable is the quarterly
rate of growth of deposits at individual banks. Depositors’ decision to make deposits in a
particular bank should be affected by the interest rate offered by the bank relative to interest
rates offered by other banks. For this reason, we use the difference between the interest rate of
the individual bank at a given time from the average for all banks at this time, rather than

simply the interest rate of the individual bank.
Also, we focus on one particular interest rate, the interest rate of foreign currency
time deposits. We do this for two reasons. First, by using a narrow category of deposits, we
make sure that shifts in deposit composition do not contaminate the interest rate series.
Second, foreign exchange time deposits are overwhelmingly the largest category of deposits,
and thus it makes sense that savers would choose to make deposits on the basis of this interest
rate (if interest rates are crucial to their choice of bank).
In addition, bank characteristics may affect depositor perceptions. However, it should
be noted that disclosure about bank performance was fairly limited in Croatia in the 1990’s.
Banks were required to publish audited annual reports, and banks offers of interest rates and
other deposit conditions were also public knowledge. However, banks were not required to
provide any higher frequency information about themselves, and the Croatian National Bank,
the regulatory institution, did not publish any further bank data. Central bank analysts did
publish two overviews of bank performance during 1997, one of which used peer group data

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(Kraft and George 1997) and the other of which pointed out the dangers of rapid growth and
singled out a set of rapidly-growing banks (Šonje 1997).
A crucial element in depositor behavior towards bank risk is the existence of deposit
insurance. A Law on Deposit Insurance was passed in 1994 (Government Gazette 44, 3, June
1994). However, enabling legislation was only passed much later, providing for the collection
of the first insurance premia in mid-1997 and the introduction of limited insurance (full
coverage of all household savings deposits up to 30,000 HRK, and 75% of the amount of
deposits between 30,000 and 50,000 HRK) was announced for January 1, 1998. Thus, while
insurance was not in place in 1996 and 1997, it was expected in the immediate future.
Furthermore, the experience of the early 1990’s could easily have lead savers to
believe that the government would not tolerate bank failures. The second, third, fourth and
fifth largest banks in the country were clearly insolvent as of 1995, and were taken over and
recapitalized by the government in 1995 and 1996. This, and the rather politicized banking
environment, could well have created expectations either that banks would not be allowed to

fail, or that an implicit government guarantee was available. Only in March 1999, when four
banks were sent to bankruptcy, did it become entirely clear that failures would happen and
that deposit insurance coverage was limited.
Given this situation of a perception of strong government guarantees, one would
expect that depositors would be relatively indifferent to bank risk in allocating their deposits.
However, it still seems important to control for bank characteristics in modeling deposit
allocation. For one thing, bank size could impact on the convenience of making deposits and
on name recognition. For another, even if a relatively limited number of depositors chose
banks on the basis of perceived soundness, indicators of solvency would be relevant. We
therefore include Tier 1 capital to asset ratios as a way of seeing whether this very broad
indicator of soundness affected depositors’ behavior, with the caveat that depositors would
only have had the previous year’s end-year figure to work with. However, capital asset ratios
change slowly in quarterly data.
We intentionally avoid using asset quality data as an indicator of bank soundness for
two reasons. First, such data was not available at all to the public, since it was not disclosed in
annual reports or in central bank publications. Second, the data before 1999 was clearly
unreliable. In several bank failures, asset quality was found to be very poor upon failure, but
previous call reports indicate minimal problems. Bank supervisors had been unable to ensure
accurate reporting in many cases.
In addition, we control for macroeconomic conditions that would shift the rate of
growth of deposits from quarter to quarter. We use the rate of growth of real GDP and
inflation to pick up changes in income and activity.

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Finally, we use dummy variables for the period before, during and after the banking
crisis. These dummies are interacted with the interest rate differential term to allow us to pick
up the changes, if any, in deposit interest elasticity over the three periods.
Before proceeding to describe the regressions, it should be noted that we are testing
the interest elasticity of deposits and not the relationship between perceived bank risk and
interest rates on uninsured bank liabilities. The latter relationship is indicative of the potential

level of market discipline. Martinez Peria and Schmukler (2000) have analyzed this effect for
a set of Latin American countries, and Ellis and Flannery (1992), Brewer and Monschean
(1994) and Keeley (1990) have analyzed this effect for U.S. banks. We argue that interest rate
differentials at Croatian banks in the pre-crisis period were mainly generated by aggressive
banks’ desire to grow rapidly, and not by depositors’ “punishing” perceived risk-takers.
However, to test for such “market-discipline” behavior, we have included the bank
characteristic variables, log total assets and Tier 1 capital ratio, in our specification. Given the
low credibility of deposit insurance in Croatia, we cannot a priori dismiss the hypothesis that
depositors “punished” risky banks with higher deposit interest rates even after the
introduction of deposit insurance in the beginning of 1998.
The regressions are run on quarterly data spanning the third quarter of 1996 and the
third quarter of 2003. The bank-by-bank data are taken from Croatian National Bank call
reports, while the macroeconomic data are taken from the CNB Bulletin and the Bulletin of
the Central Bureau of Statistics. Interest rate variables are contemporaneous, but the bank
characteristics variables are lagged one quarter. This effectively means using the value at the
end of the previous quarter, immediately before the start of the current quarter.
Because of the possibility of biased results from OLS due to short-time series (Judson
and Owen (1999), we estimated several alternative models: OLS, fixed effects, and
GMM/Arellano-Bond two step. Below we will focus on the OLS results, which we prefer, but
we will note where conclusions are not robust to estimation methods.

9
Table 2: Determinants of Growth Rate of Foreign Exchange Time Deposits
(1) (2) (3) (4)
OLS OLS
macro
Fixed
effects
Arellano-
Bond

Constant 0.380 0.372 1.524
(3.44)** (3.34)** (4.15)**

Interest differential 0.022 0.022 0.043 0.086
(3.25)** (3.25)** (4.41)** (3.78)**

Interest differential x -0.050 -0.050 -0.050 -0.063
Crisis dummy (4.01)** (4.03)** (3.78)** (2.75)**

Interest differential x -0.017 -0.017 -0.033 -0.063
Post-crisis dummy (1.56) (1.57) (2.48)** (2.61)**

Deposit growth (-1) 0.044 0.045 -0.006
(1.65)+ (1.68)+ (0.21)

Foreign bank dummy 0.077 0.077 0.030 -0.109
(3.91)** (3.92)** (0.90) (1.87)+

Log total assets (-1) -0.014 -0.014 -0.096
(1.93)+ (1.90)+ (3.61)**

Tier 1 capital/assets (-1) -0.110 -0.107 -0.236 -1.343
(1.47) (1.43) (1.73)+ (10.07)**

Crisis dummy -0.154 -0.153 -0.161 -0.152
(6.73)** (6.63)** (6.73)** (7.11)**

Post-crisis dummy -0.134 -0.092 -0.130
(6.90)** (6.85)** (5.37)**

Euro-effect dummy 0.072 0.092 0.091 0.046
(1.59) (1.84)+ (1.99)** (3.87)**

Real GDP growth 0.002+
(1.84)

Retail price inflation 0.001
(0.10)

Adjusted R-squared 0.070 0.071 0.073 0.085
F-statistic 10.97 9.44 11.84 609.03$
(probability) 0.000 0.000 0.000


Total observations: 1333
** significant at 1% * significant at 5% + significant at 10%
$ J-statistic instead of F-statistic


The most important message is this: the interest-elasticity of deposits is positive
during the rapid expansion period, and then actually becomes negative during the crisis
period. The point estimate is -0.028, and the probability of this value being equal to 0 on the
Wald test is p=0.008. Furthermore, after the crisis, the interest-elasticity rises relative to the

10
crisis period and becomes barely positive at 0.005. A Wald test shows that we cannot reject
the hypothesis that the post-crisis elasticity is zero (p = 0.574). Although the conclusions
about the signs of elasticity during the crisis and after it are not robust to alternative
specifications, the general picture of a sharp fall in elasticity during the crisis is robust.

To complete the picture, note that the dummy for foreign banks is significant for the
whole period, indicating that foreign banks showed more rapid deposit growth. We tested for
changes in the foreign bank effect by interacting the foreign bank dummy with the crisis and
post-crisis dummies (results not shown). During the crisis period, the foreign bank dummy
rises, but the interacted crisis-foreign bank dummy is not significant at conventional levels
(t=1.71). However, this is not the whole story, since foreign banks offered lower deposit
interest rates than domestic ones (Galac and Kraft 2000). The significant negative interest-
elasticity during the crisis period thus implies an even larger differential between deposit
growth at foreign banks and that at domestic banks during the crisis period.
The interaction of the foreign bank dummy with the post-crisis dummy was highly
insignificant, suggesting that there was no change in the foreign bank effect after the crisis
was over.
Thus, the story of a sharp shift from a situation in which deposits had a high positive
interest elasticity to one in which high deposit interest rates were taken as a sign of heightened
risk is confirmed. In addition, we can note that both the log total assets and capital-adequacy
ratio variables were “incorrectly” signed in the sense that larger, better capitalized banks
experienced slower deposit growth. This further adds to the argument that depositors did not
perceive differences in bank risk as important in their deposit allocations before the crisis.
At the same time, the zero interest elasticity of deposits in the post-crisis period
suggests that depositors remained concerned that high deposit rates might signal greater risk.
Furthermore, this zero elasticity suggests that deposit insurance was not considered credible.
This is hardly surprising, since deposit insurance payouts were extremely slow during the
1998-99 bank failures. In some cases, the period between the blocking of the bank’s accounts
and the payment of insurance was almost two and half years. Even if interest were paid on
deposit liabilities, liquidity-constrained depositors would certainly not be indifferent to failure
in such a situation.
We also tested for changes in depositors’ risk-perceptions by interacting the dummies
for the crisis and post-crisis period with the bank characteristic variables, log total assets and
tier 1 capital ratio (results not shown). The interacted variables were insignificant. It would be
hasty, however, to conclude from this that Croatian depositors did not “punish” banks

perceived to be risk in the crisis and post-crisis periods. Rather, a more plausible
interpretation of the findings would be that Croatian depositors presumed foreign banks to be
less risky throughout the whole period, and that they perceived banks offering high interest
rates to be risky during the crisis and to an extent after it. The continued perception by at least

11
some depositors that high deposit interest rates are a sign of risk could help explain the
estimated zero interest elasticity in the post-crisis period.

4. Deposit interest rates and the causes of bank failures

Now that we have shown that banks were able to gain increased access to funding by
raising deposit interest rates, we can examine whether there was a connection between high
deposit interest rates and bank failure. Most research suggests that bank failures occur as a
result of credit boom and bust cycles (see Logan, 2000), recklessness and fraud, and poor
management. All other frequently cited reasons can be classified as belonging to the latter
category (see Honohan 1997).
Furthermore, bank failures are rare events. This makes it hard to study their causes and
consequences using econometric techniques. Actually, they appear in clusters during times of
political or economic instability or transition, and then they are reasonably referred to as a
"banking crisis" (Hardy, 1998). This is why most empirical studies examining causes of bank
failures are cross-section analyses of pre-banking crisis bank characteristics that can be
reasonably conjectured to have caused the failures during the crisis.
The empirical literature on leading indicators of bank failures suggests that leading
indicators can be roughly categorized into five classes: CAMELS grades, international
agencies' ratings, market prices of bank stocks and subordinated debt, (standard) balance-
sheet and income statements financial ratios, and other (non-standard) measures of bank risk
and financial strength.
Regarding the first two classes, there is increasing evidence that traditional CAMELS
grades and especially international credit ratings have limited bank failure prediction

capabilities in emerging market countries (Rojas-Suarez, 2001). Furthermore, there is some
empirical evidence on the weakness of market prices in predicting bank failures not only in
the less developed financial systems such as those of South-East Asia (Bongini et al., 2001),
but also in the most developed banking systems with deep and liquid markets such as that of
the US (Gilbert et al., 2001). This evidence contests the logical expectation that CAMELS
grades, international agencies' ratings, and market price risk premia - all containing implicit
assessments of the probability of a bank's failure by the most informed market participants –
should be closely correlated with the probability of bank failure.
In the case of Croatia, this discussion is somewhat academic due to lack of data. Only
one Croatian bank had been rated by an international agency prior to 1998, and only a few
banks have ever had their stocks or bonds listed on the market. Also, there is no market for
CD's. Furthermore, even though the interbank market is active in Croatia, it is concentrated on
trading in very short term instruments whose prices carry little information on individual

12
banks' risk premia. Finally, the Croatian National Bank, which supervises commercial banks,
had not introduced CAMELS grades prior to the banking failures studied here.
The remaining two classes of potential explanatory variables for our bank failure
prediction model are standard balance sheet and income statement ratios and other non-
standard indicators of banks' financial condition and risk profile. The indicators most
commonly found in empirical studies can further be categorized according to specific risks or
strengths that they measure or proxy (see Appendix Table 1).
1
We included most of these
indicators in our initial analysis, and added some additional ones to measure or proxy specific
risks faced by Croatian banks of the mid-90's (for more information see the detailed
discussions of these risks in Kraft 1999; Šonje and Vujčić, 1999; and Jankov 2000).
We compiled a list of 38 potential explanatory variables for bank failure prediction,
including 33 ratios, 2 interval values, and 3 dummies. The three dummies are: new (founded
after 1989), foreign (founded as a foreign subsidiary), and "too big to fail" (by our own expert

judgment). Two interval-type variables, to be used for robustness checks, are total assets and
total off-balance sheet assets. The remaining 33 "ratios" include standard financial ratios for
banks, such as return-on- average-assets and Basel-type capital adequacy ratios, but also a
number of less standard measures and "quasi-ratios" (see Appendix Table 2).
Choice of the dependent variable required making several expert judgments. The first
decision was whether to include both distressed and failed banks. Since the definition of
distress is intrinsically subjective, and in practice often based on perceived levels of the very
variables that are included in the candidate explanatory variables list, we chose to consider
those banks that eventually entered into a bankruptcy or a liquidation process (14 banks) or
had been taken into state receivership and rehabilitated at taxpayers’ expense (2 banks).
Exceptionally, we also consider one bank as failed that does not formally meet these criteria,
but is known to have been insolvent in 1999-2000.
2

A second, related decision was to extend the time horizon for failure of bankrupt and
liquidated banks, since most actually entered into bankruptcy or liquidation only after the
1998-99 crisis period, due to the unusually slow legal process of bank closure in Croatia. To
be precise, we labeled as failed all banks operating at the beginning of 1998 that ceased
operations before 2003 due to observable effects of the banking crisis.
Similarly, we extended the independent variable data set to 1995, that is up to three
years before the crisis started, to evaluate the predictive power of our models at three different
forecast horizons. We did this because we held a prior belief that some risky bank behavior
would show persistence (i.e. its measure or proxy will enter the best model at every lag),

1
See for example Logan, 2000, Gonzalez-Hermosillo, 1999, Hanousek, 1999 and Rojas-Suarez, 2001
2
The bank was found to be insolvent by central bank examiners. A central bank administrator was appointed,
and the announcement of his appointment led to a bank run. The bank was temporarily closed, and then
recapitalized by government payment of back interest on certain government securities held by this bank and

others. Later the bank was sold.

13
while some other behaviors could be reasonably related to failure even if they happened only
once (i.e. deadweight of one year's overly risky investment or chronic illiquidity at the onset
of crisis).
Since all of the failed banks were in operation by 1996, and all but one were in
operation by 1995, all of the failed banks are included in our analysis. Two foreign owned
subsidiaries that only started their operations in 1997 and the one foreign branch established
were excluded from the analysis, since their operations were unusual enough to produce
extreme outliers on most candidate variables. This resulted in a sample of 17 failed and 40
surviving banks. Also, since not all candidate variables were measured in all three years of
interest, and some banks started operating during this period, not all variables that are
measured in all three years have measurements on all banks for all years.
Our model building strategy involved two steps: selecting variables that would best
discriminate between failed and non-failed banks, and then using these variables to build logit
failures models. We begin the selection process by testing for normality using the
Kolmogorov-Smirnov test with Lilliefors' significance correction, and the Shapiro-Wilk test
for variables with less than 51 observations (see Appendix Table 3). The tests found that
normality could be rejected at the five percent significance level for 30 of the 35 variables
tested. Even among the 5 variables for which normality could not be rejected, normality could
not be rejected for two forecast periods for only 2 variables, and there were no variables for
which normality could not be rejected for all three forecast periods.
Having concluded that by and large the explanatory variables are non-normally
distributed, we then used the nonparametric Mann-Whitney U-test (see Table 4 in the
Appendix) for the difference in medians between the group of failed banks and the group of
survived banks. At the ten percent (two-tailed) significance level, the test found four variables
that had statistically significant differences in medians for every forecast horizon. It found an
additional three variables that were statistically significant at two out of three horizons, and
seven variables that were significant at only one horizon. The seven variables significant at

more than one horizon and their group medians with respect to the dependent variable are
shown in Table 3.

Table 3. Bank Failure Final Sample Variables
Forecast
horizon
Group DR LIQ CAR RLAR LR OHER CM
3-year F=0 3,9 0,1 24,5 47,8 103,1
F=1 6,4 -0,1 26,4 52,6 99,3
Total 4,4 0,0 24,9 50,2 101,0
Mann Whitney U Test p 0,0001 0,0035 0,1330 0,2755 0,4294
2-year F=0 3,2 12,7 35,4 3,8 21,6 52,7 95,4
F=1 7,4 -13,3 20,2 10,2 26,2 49,9 86,4
Total 4,4 8,2 31,2 5,5 22,7 50,6 95,1
Mann Whitney U Test p 0,0000 0,0009 0,0126 0,0533 0,0143 0,0752 0,0494
1-year F=0 3,0 13,2 26,8 2,9 15,0 55,9 74,2
F=1 5,8 -2,3 15,2 11,5 18,7 43,4 57,0

14
Total 3,5 8,4 24,4 3,6 16,2 50,9 72,6
Mann Whitney U Test p 0,0001 0,0002 0,0024 0,0455 0,0066 0,0396 0,0347

DR represents the annual average of monthly volume weighted average deposit rates
on new or renewed foreign currency denominated deposits. LIQ is the annual average of daily
ratios of non-borrowed excess reserves to required reserve deposit base. CAR (capital
adequacy ratio) is just a year-end standard Basel I type regulatory capital to risk weighted
assets ratio. RLAR is a year end risky loans to total assets ratio, where risky loans are defined
as large and very large loans as well as total exposure to connected parties. Computed
analogously to DR, the LR variable represents the loan rate on domestic currency
denominated new loans. The only income statement indicator among the selected variables,

OHER is the year-end proportion of overhead expenses in total expenses. Finally, the only
balance-sheet variable in the selected group, CM (currency mismatch indicator) is the ratio of
total foreign currency assets and foreign currency deposits.
These variables represent the features most closely associated with the observed bank
failures. They variables also offer hope of explaining why "natural" candidate predictors of
bank failures - low profitability, high levels of bad assets, and rapid growth – are not useful
for explaining 1998-99 bank failures in Croatia. Looking back to Table 3 it is easy to see that
the first four variables all have the expected relative values ("signs") at all lags. Thus, the
failed banks as a group have higher deposit rates, lower non-borrowed excess reserves, lower
capital adequacy and higher levels of risky loans. They also have higher loan rates at all lags,
which is consistent with the anecdotal evidence that these banks attracted riskier clients and at
the same time mispriced their risk. This contrasts with the more prevalent cross-country
finding that low spreads are strongly associated with bank failures (Rojas-Suarez, 2001),
perhaps because sudden appearance of fierce competition for deposits raises deposit rates,
thus squeezing the margins and causing failures of the internally most inefficient banks.
The remaining two variables, OHER and CM, are both insignificant at lag t-3, and for
the other two forecast horizons their relative values are difficult to interpret.
Returning to the first four variables, each highly significant and with expected and
persistent sign, it is worthwhile to explore possible causal relationships between them and
bank failure. The most likely explanation of the causality between high deposit rates and bank
failures has already been suggested in the first three parts of this paper: aggressive banks used
high deposit rates to fund their excessively risky business strategies, which eventually led
them to failure. The negative relationship between the narrow measure of liquidity (provided
by the non-borrowed reserves ratio) and the failure variable can be explained by a temporary
failure of the domestic money market during the early stages of the banking crisis, or in a
wider context, by the poor liquidity of all economic agents preceding the 1999 recession that
made it difficult for illiquid banks to raise funds in those times of need (Šonje, Faulend and
Šošić, 2001). Alternatively, one could cite DeJuan in saying that, for banks, chronic illiquidity

15

is almost always a sign of (hidden) insolvency (De Juan, 1996). For chronically illiquid
banks, failure is just a question of "when".
Deposit rates and narrow liquidity are by far the most significant predictors of bank
failure in our sample, with Mann Whitney U test significance levels well below one percent.
The other two important variables are capital adequacy and risky loans. Finally, loan interest
rates can be interpreted as a measure of negative client selection. They are also highly
significant at first two lags, and only mildly insignificant at lag t-3, which makes them a good
predictor of bank failure as well.
Having selected the variables that best distinguish between failed and non-failed
banks, we proceed to select the "best" (most parsimonious) logit models separately for each of
the three forecast horizons. Combining variables from different forecast horizons proved
impractical because of the high correlation between the same variable at different horizons
(e.g. capital adequacy in 1996 is highly correlated with capital adequacy in 1997).
The results of this exercise provide further confirmation of an unusually strong
connection between deposit rates and bank failures. All the variables found significant by the
Mann Whitney U test for a particular horizon were considered for inclusion in the final model
for that forecast horizon. Then, the model with the lowest SIC statistic
3
was chosen as the
"best" forecasting model for that horizon. The best models are presented in Table 4.

Table 4. The "Best" Logit Specifications
3-yr. forecast
Optimal cut-off = 0,29 B S.E. Wald Sig. 95.0% C.I.for EXP(B)
Lower Upper
Intercept -5,22 1,602 10,61 0,00 -8,36 -2,08
"dr95" 0,91 0,300 9,15 0,00 0,32 1,50
"liq95" -3,39 1,441 5,54 0,02 -6,22 -0,57
* Total obs. = 57, missing = 10; Total error = 21,28%, for failed = 20,0%, for survived = 21,88%

2-yr. forecast
Optimal cut-off = 0,33 B S.E. Wald Sig. 95.0% C.I.for EXP(B)
Lower Upper
Intercept -5,44 1,677 10,52 0,00 -8,73 -2,15
"dr96" 0,97 0,309 9,93 0,00 0,37 1,58
"liq96" -0,04 0,020 4,55 0,03 -0,08 0,00
* Total obs. = 57, missing = 6; Total error = 13,73%, for failed = 11,76%, for survived = 14,71%

1-yr. forecast
Optimal cut-off = 0,31 B S.E. Wald Sig. 95.0% C.I.for EXP(B)
Lower Upper
Intercept -3,85 1,040 13,68 0,00 -5,89 -1,81
"rlar97" 0,06 0,032 3,55 0,06 0,00 0,12

3
In an earlier version of this paper (Kraft and Galac, 2004) stepwise selection procedures based on the log-
likelihood statistic were used to select slightly different set of best models. The SIC statistic selection criterion
used in this paper should yield a better selection, considering the small size of our sample in conjunction with the
greater penalty for lost degrees of freedom that is associated with the SIC measure of fit. The fact that the best
model at each forecast horizon includes the deposit rate variable in both papers then makes our concluding point
about the importance of deposit rates in Croatian bank failure prediction even stronger.

16
"dr97" 0,57 0,191 8,79 0,00 0,19 0,94
* Total obs. = 57, missing = 3; Total error = 18,52%, for failed = 17,65%, for survived = 18,92%



The DR variable entered the best model for each of the forecast horizons. More importantly,
only two other variables improved model fit when added to the DR variable: the LIQ variable

for the 3-year and the 2-year ahead forecast and the RLAR variable for the 1-year ahead
forecast. The second best individual predictor of bank failure, the variable LIQ also entered
the second best model for the 1-year forecast (not shown in the table). This strong relationship
between DR and LIQ as predictors of bank failure in Croatia has been already documented
and interpreted in earlier studies (Kraft 1999).
Furthermore, it seems that the deposit rate variable DR is not only the best individual
predictor of 1998-99 failures of Croatian banks, but it is a better predictor than all viable
combinations of other good individual predictors. To test this, we repeated the logit model
selection process, this time without the DR among the variables considered. The comparison
of the model specification containing only the DR variable, and the specification containing
the best model not containing the DR variable, for each forecast horizon separately, shows
that the DR model is superior (at 1-year horizon on the grounds of parsimony, since the next
best model has a lower AIC statistic) to all non-nested alternatives, as is evident from Table 5.
This result is strikingly similar to the findings of a study of bank failures during the mid-
nineties in the Czech Republic (Hanousek, 1999).

Table 5. Comparison of DR Models with "Best" Non-Nested Alternatives
AIC SIC
Variables in the model


3-year forecast
DR 45,55 49,25
LIQ 55,89 59,59

2-year forecast
DR 44,13 48,00
CM, LR, LIQ 51,93 59,65

1-year forecast

DR 57,64 61,61
RLAR, CM, LR 55,86 63,82



As a check, we verified that the DR variable measures a unique characteristic of bank
behavior in the 1995-97 period by re-estimating the DR-only model for each forecast horizon
and for each of the five control variables (balance sheet size BS, off-balance sheet size OBS,
foreign subsidiary dummy FOR, too-big-to-fail dummy TBTF, and new bank dummy NEW).

17
As expected, including any of the size variables in the regressions was fruitless. Their
individual Wald statistics were highly insignificant, while the overall model fit did not
change. Also, all interactions between the DR variable and the three dummies were highly
insignificant, as were the main effects of the dummies in those regressions.
These findings strongly suggest that high bank deposit rates in the 1995-97 period are
the most powerful predictors of bank failures during the 1998-99 banking crisis in Croatia.
Moreover, their individual predictive power cannot be exceeded even by a carefully chosen
combination of other strong individual predictors of bank failure. Finally, there are no
interactions between deposit rates and other important measurable qualitative bank
characteristics that can further contribute to bank failure predictions.

5. Conclusions
The findings in this paper lead us to the following conclusions:
1) “Market-stealing” via high deposit interest rates can be an effective strategy in banking
markets characterized by substantial competition. In Croatia, depositors appear to have been
relatively slow to link high deposit rates with increased portfolio risk. We suggest that this
was due to perceptions of an implicit government guarantee, along with depositor
inexperience. We would stress that such circumstances are common in newly-liberalized
financial markets and are unlikely to have been unique to Croatia.

2) Gaining credibility for deposit insurance after a bank crisis can be difficult. In the Croatian
case, where long delays in deposit insurance payout caused substantial problems for
depositors, it is clear that credibility has not been fully restored even four years after the bank
crisis, as is suggested by zero interest elasticity of deposits.
3) Foreign banks from advanced countries enjoy a reputational advantage that allows them to
raise deposits despite offering lower interest rates. They were perceived by Croatian
depositors as “safe havens” during the 1998-99 banking crisis. This, of course, is one of the
reasons for the rapid expansion of foreign banks’ market share in Croatia and almost all of the
transition countries.
4) The link between high deposit interest rates and portfolio risk predicted by theory is
confirmed in Croatia. Although deposit interest rates are not the only predictor of failure, they
are in fact the best predictor of failure in the Croatian case.
5) At the same time, the Croatian case shows the inability of newly created supervisory
authorities to effectively limit risk-taking. In fact, we believe that supervisors did not have a
good handle on how much risk individual banks were taking, nor on the quality of banks’ loan
portfolios. In a situation where bank supervision is undeveloped, or where liberalization has
put greater demands on supervision than it is able to bear, deposit interest rates may be a very
useful tool to focus supervisory attention.

18
6) Monitoring and controlling deposit interest rates seems much easier than monitoring and
controlling overall portfolio risk, and this makes the Hellman-Murdock-Stiglitz suggestion of
limiting deposit interest rates tempting. However, one should not be naïve about banks’
ability to get around such regulation. Only the implementation of a comprehensive reporting
framework for interest rates like the U.S. Annual Percentage Rate (APR) can prevent banks
from offering higher interest rates to customers than they report to the authorities. Usually,
drafting, passing and enforcing such regulations takes time; in Croatia, for example, such
regulations were introduced well after the banking crisis.
Furthermore, even if one has a uniform system of interest rate reporting in place, risk-
loving banks may find other ways to steal market share. These methods most likely will be

less transparent than simply offering higher deposit rates.
Finally, one could argue that there is nothing wrong with banks offering high interest
rates, if the supervisory authorities use this as a sign of possible increased portfolio risk and
that triggers enhanced supervisory attention and, if necessary, prompt corrective action. The
real problem is not that risk-loving banks offer high deposit interest rates, but that they make
bad loans that eventually bring down the bank. It is not clear that limiting deposit interest
rates will cut off funding for high risk projects, or substantially decrease the risk appetite of
unsound banks. Thus, it may be more effective to use high deposit interest rates as a signal
that helps prioritize the use of scarce supervisory resources in a newly-liberalized context,
than to try to administratively limit deposit rates and perhaps derive a false confidence that
bank risk-taking has been limited. However, in a situation in which bank supervision is very
weak, and prompt corrective action powers do not exist or are very limited by legislation,
deposit interest rate limitation might be better than nothing.

19
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23
APPENDIX

Table 1. Typical leading indicators in bank failure prediction research


Credit risk indicators:
- loan growth, provisions to assets ratio, balance sheet growth, classified to total assets,
non-performing loans to total loans

Liquidity risk indicators:
- short term assets to short term liabilities, inter-bank loans to total liabilities, loans to
deposits, loans to assets

Concentration risk indicators:
- large exposures to total assets, large deposits to total deposits, sectoral loan shares, net
interest income to total income

Capital strength:
- total assets, capital adequacy ratio, capital to assets, return on assets, return on equity

Efficiency:
- net-interest margin, interest rate spreads, overhead expenses to assets

Other strengths and hazards:
- age, "too big to fail" dummy, strong "parent" dummy, "foreign" dummy, deposit rates,
loan rates, insider loans to assets



24
Table 2. Definition of variables


T
yp
e No. Code Descri
p
tion Measured risk Ex
p
ected si
g
n

De
p
endent FNAR Narrow failure indicator - bankru
p
t, li
q
uidated, rehabilitated
Dumm
y
1 NEW Founded after 1989 indicato
r
Control +
2 FOR Founded as forei
g
n dau
g
hter indicato
r
Control -
3 TBTF Too bi
g
to fail b
y
ex
p
ert
j
ud
g
ement indicato

r
Control -
Interval 4 BS Total balance sheet size Control -
5 OBS Total size of off-balance sheet items Control +
Ratio 6 CAR Basel 1 ca
p
ital ade
q
uac
y
ratio Ca
p
ital stren
g
th -
7RLAR Ver
y
risk
y
loans / total BS assets Risk-aversion +
8RIAR Ver
y
risk
y
investments / total BS assets Risk-aversion +
9 ROBAR Uncollateralized off-bs assets / total BS assets Risk-aversion +
10 RMBAR Mort
g
a
g

e backed BS&OBS claims / total BS assets Risk-aversion +
11 NPAR Non-
p
erformin
g
BS&OBS assets / total BS assets Asset
q
ualit
y
+
12 IMPAR Ima
p
ired BS&OBS assets / total BS assets Asset
q
ualit
y
+
13 CM Forei
g
n currenc
y
assets / fc de
p
osits Forei
g
n exchan
g
e-
14 MM Short term assets / st de
p

osits Li
q
uidit
y
-
15 SD
I
1 - Sum
(
sector ass. s
q
uare
)
/ Total ass. s
q
uare Sectoral -
16 NCSSR Non-core sources / sources Li
q
uidit
y
+
17 FAAR Fixed assets / assets Li
q
uidit
y
+
18 PIAR Permanent investment / assets Li
q
uidit
y

+
19 AARER Accruals&arrears / revenues&ex
p
enses Li
q
uidit
y
+
20 RO
A
Return on end-
y
ear assets Profitabilit
y
-
21 ROE Return on end-
y
ear e
q
uit
y
Profitabilit
y
-
22 PAR Provisions / assets Asset
q
ualit
y
+/-
23 PCR Provisions / e

q
uit
y
Asset
q
ualit
y
+/-
24 LTIAR Lon
g
-term investment / assets Li
q
uidit
y
+
25 LDR Loans / de
p
osits Li
q
uidit
y
+
26 DLR De
p
osit
p
lacements / loan sources Li
q
uidit
y

+
27 MGPPR Mone
y
&
g
ov.
p
a
p
er / core sources Li
q
uidit
y
-
28 NIM Net interest mar
g
in Efficienc
y
-
29 OHAR Overhead ex
p
. / assets Efficienc
y
+
30 OHER Overhead ex
p
. / ex
p
enses Efficienc
y

+
31 OBBR Off-bs assets / bs assets Control +/-
32 BSA
G
Annual balance sheet
g
rowth rate Growth +
33 OBSA
G
Annual off-balance sheet
g
rowth rate Growth +
34 LR Interest rate on credits in national currenc
y
, ann. av
g
. Adverse selection +
35 DR De
p
osit rate on forei
g
n currenc
y
savin
g
s, ann. av
g
.Moral hazard +
36 SPR LR-DR Efficienc
y

-
37 LI
Q
Non-borrowed excess reserves / re
q
. res. de
p
osit base Li
q
uidit
y
-
38 CRA
G
Annual loan
g
rowth rate Growth +